Tensor

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. val F: Tensor.type

   

5. val One: F

   

6. val Zero: F

   

7. def apply(dt: DataType)(data: Array[Elem], offset: Int, dims: Dims): Tensor[dt.type]

   

8. def apply[H](h: H)(implicit ev: Build[H, Float]): Tensor[Float32.type]

   

9. def apply(dt: DataType, dims0: Dims)(storage0: Storage[Elem]): Tensor[dt.type]

   

10. final def asInstanceOf[T0]: T0

    

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11. def clone(): AnyRef

    

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12. def concatenate(dt: DataType, axis: Long)(tensors: Seq[Tensor[dt.type]]): Try[Tensor[dt.type]]

    

    equivalent to numpy.concatenate https://docs.scipy.org/doc/numpy/reference/generated/numpy.concatenate.html

13. def const(dt: DataType)(x: Elem, axes: Axes): Tensor[dt.type]

    

14. def convertAxesToTensor(s: Axes): Tensor[Int64.type]

    

    This is the inverse of convertDataToAxes, represent the axes as a 1-D tensor with type DataType.Int64

15. final def eq(arg0: AnyRef): Boolean

    

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16. def equals(arg0: Any): Boolean

    

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17. def finalize(): Unit

    

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18. final def getClass(): Class[_]

    

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19. def hashCode(): Int

    

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20. def identityMatrix(dt: DataType, size: Long): Tensor[dt.type]

    

21. final def isInstanceOf[T0]: Boolean

    

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22. def load(path: Path, dt: DataType, axes: Axes): Resource[IO, Tensor[dt.type]]

    

23. def loadBytes(bytes: Array[Byte], dt: DataType, axes: Axes): Try[Tensor[dt.type]]

    

24. def loadMappedRowMajorTensor(dt: DataType, path: Path, axes: Axes): Resource[IO, Tensor[dt.type]]

    

25. def loadMappedTensor(dt: DataType, path: Path, dims: Dims): Resource[IO, Tensor[dt.type]]

    

26. def map2(dt: DataType)(x0: Tensor[dt.type], y0: Tensor[dt.type])(f: (Elem, Elem) ⇒ Elem): Try[Tensor[dt.type]]

    

    This is a broadcasting map2 following numpy broadcasting rules

27. def matrix(dt: DataType)(data: Array[Elem], offset: Int, height: Long, width: Long): Tensor[dt.type]

    

28. final def ne(arg0: AnyRef): Boolean

    

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29. final def notify(): Unit

    

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30. final def notifyAll(): Unit

    

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31. def of[D <: Aux[E], E, H](dt: D)(h: H)(implicit ev: Build[H, E]): Tensor[dt.type]

    

32. def one(axes: Axes): F

    

33. def save[D <: DataType](path: Path, tensor: Tensor[D]): IO[Unit]

    

34. def scalar(dt: DataType)(x: Elem): Tensor[dt.type]

    

35. def stack(dt: DataType, axis: Long)(tensors: Seq[Tensor[dt.type]]): Try[Tensor[dt.type]]

    

36. final def synchronized[T0](arg0: ⇒ T0): T0

    

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37. def toString(): String

    

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38. def unchunk(dt: DataType, axis: Long)(tensors: Seq[Tensor[dt.type]]): Try[Tensor[dt.type]]

    

    This is the opposite of chunk, which glues together tensors along a given axis

39. def vector(dt: DataType)(data: Array[Elem]): Tensor[dt.type]

    

40. final def wait(): Unit

    

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41. final def wait(arg0: Long, arg1: Int): Unit

    

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42. final def wait(arg0: Long): Unit

    

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43. def zero(axes: Axes): F